Exposure to traffic exhaust is common in urban areas and its components have been found to be associated with lung cancer and leukemia in epidemiological studies. Determining whether or not these associations are causal in nature has been especially limited by the lack of historical individual-level exposure data. Recently, there have been calls for the development of exposure maps showing modeled concentrations of ambient air pollution using geographic information systems (GIS) to investigate long-term health effects. A study subject's residence can be a good predictor of exposure if suitably modeled levels of air pollution are achieved at a proper spatial scale. Typically, modeling of air pollution has been done either at a regional (air basin) or localized level. The regional approach results in coarse exposure maps, which lack the spatial resolution needed for epidemiological studies and can result in exposure misclassification. The localized models achieve a proper spatial scale but require complex data inputs and are too data intensive for mapping exposure over wide areas. We have previously developed GIS traffic exposure maps at an appropriate scale for epidemiological studies that account for properties of wind and dispersal behavior of specific pollutants. In this study, we propose to further develop this model by accounting for the surface texture of the landscape using GIS land use layers. We also propose to evaluate and validate this and two other in-house developed models and four external traffic exhaust exposure models in one California county with NO2 field measurements using passive diffusion tubes. We will compare the predicted exposure level from each model to actual NO2 concentrations at each monitored location. We will assess the amount of bias due to exposure misclassification for each model by geocoding the addresses of lung cancer cases from the California Cancer Registry and a random control series and comparing the predicted and observed NO2 measurements. Finally, we will develop a series of historic traffic exhaust exposure maps using the best evaluated traffic model using retrospective data on traffic counts, land use, meteorology, point sources, and ambient air monitoring data. These resulting exposure maps could be applied to existing cohorts of study subjects to assign previous exposures which would dramatically reduce the time and cost of expensive prospective studies of traffic exhaust exposure and cancer risk.